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Manufacturing Yield Improvement by Clustering

Karim, Azharul, Halgamuge, Saman, Smith, A. J. R., & Hsu, A. L. (2006) Manufacturing Yield Improvement by Clustering. Neural Information Processing, 4234, pp. 526-534.

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Abstract

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

Impact and interest:

5 citations in Scopus
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ID Code: 46235
Item Type: Journal Article
DOI: 10.1007/11893295_58
ISSN: 0302-9743
Divisions: Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Deposited On: 28 Sep 2011 15:43
Last Modified: 01 Mar 2012 00:12

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